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High Resolution Phonon-assisted Quasi-resonance Fluorescence Spectroscopy
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Published on: June 28, 2016

Photon level chemical classification using digital compressive detection.

David S Wilcox1, Gregery T Buzzard, Bradley J Lucier

  • 1Purdue University, Department of Chemistry, West Lafayette, IN, USA. wilcoxds@purdue.edu

Analytica Chimica Acta
|November 14, 2012
PubMed
Summary
This summary is machine-generated.

A new digital compressive detection strategy significantly speeds up hyperspectral data collection. This method uses programmable filters to identify chemical species with minimal Raman scattered photons, outperforming older techniques.

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Area of Science:

  • Spectroscopy
  • Chemical Analysis
  • Photonics

Background:

  • High-speed chemical analysis, including hyperspectral imaging, is limited by data collection and analysis time.
  • Monitoring dynamic chemical processes requires rapid and efficient data acquisition.

Purpose of the Study:

  • To develop and validate a digital compressive detection strategy for accelerating hyperspectral data collection.
  • To demonstrate the capability of detecting chemical species using minimal Raman scattered photons.

Main Methods:

  • Utilizing a digital compressive detection strategy with programmable binary optical filters.
  • Employing a digital compressive detection instrument with a diode laser, spatial light modulator, and photon counting detector.
  • Analyzing liquid samples with varying spectral overlaps to assess method accuracy.

Main Results:

  • Successful identification of chemical species using as few as ~10 Raman scattered photons in ~30 μs.
  • Demonstrated dependence of accuracy on spectral correlation coefficients for different chemical pairs.
  • Validation of the digital compressive detection strategy as Poisson photon noise limited.

Conclusions:

  • The digital compressive detection strategy offers a significant speed enhancement for hyperspectral data acquisition.
  • This method outperforms traditional total least squares-based compressive detection with analog filters.
  • The approach is robust and effective for rapid chemical species identification and analysis.